An in vivo cytotoxicity threshold for influenza A virus-specific effector and memory CD8+ T cells

Influenza A virus infection of C57BL/6 (B6) mice is characterized by prominent CD8 T cell responses to H2Db complexed with peptides from the viral nucleoprotein (NP366, ASNENMETM) and acid polymerase (PA224, SSLENFRAYV). An in vivo cytotoxicity assay that depends on the adoptive transfer of peptide-pulsed, syngeneic targets was used in this study to quantitate the cytotoxic potential of DbNP366- and DbPA224-specific acute and memory CD8 T cells following primary or secondary virus challenge. Both T cell populations displayed equivalent levels of in vivo effector function when comparable numbers were transferred into naive B6 hosts. Cytotoxic activity following primary infection clearly correlated with the frequency of tetramer-stained CD8 T cells. This relationship looked, however, to be less direct following secondary exposure, partly because the numbers of CD8DbNP366 T cells were greatly in excess. However, calculating the in vivo E:T ratios indicated that in vivo lysis, like many other biological functions, is threshold dependent. Furthermore, the capacity to eliminate peptide-pulsed targets was independent of the differentiation state (i.e., primary or secondary effectors) and was comparable for the two T cell specificities that were analyzed. These experiments provide insights that may be of value for adoptive immunotherapy, where careful consideration of both the activation state and the number of effector cells is required.

Location rather than CD62L phenotype is critical in the early establishment of influenza-specific T cell memory

The rapid recall of influenza virus-specific CD8+ T cell effector function is protective, although our understanding of T cell memory remains incomplete. Recent debate has focused particularly on the CD62L lymph node homing receptor. The present analysis shows that although functional memory can be established from both CD62Lhi and CD62Llo CD8+ T cell subsets soon after initial encounter between naive precursors and antigen, the optimal precursors are CD8+CD44hiCD25lo immune lymphocytes isolated from draining lymph nodes on day 3.5 after influenza virus infection. Analysis of primed T cells at different times after challenge indicates that the capacity to transfer memory is diminished at the peak of the primary cytotoxic T lymphocyte response, challenging speculations that the transition to memory first requires full differentiation to effector status. It seems that location rather than CD62Lhi/lo phenotype may be the more profitable focus for further dissection of the early establishment of T cell memory.

Killer T cells in influenza

Antigen-specific CD8+ T cells play an important role in virus clearance. Here we review the current understanding of influenza virus-specific CD8+ T cell immunity in experimental mouse models and humans. The characteristics and nature of CD8+ T cell killing are discussed, as is the selection and maintenance of the influenza-specific effector and memory repertoires. Consideration is given to vaccine strategies and to the effects of ageing. Understanding the complexities of CD8+ T cell mediated immunity and memory has the potential for improving vaccine design, particularly to combat pandemics caused by newly emerging influenza viruses.

In vitro osteoblast-like cell proliferation on nano-hydroxyapatite coatings with different morphologies on a titanium-niobium shape memory alloy

The morphology of nanomaterials significantly affects their physical, chemical, and biological properties. In the present study, nano-hydroxyapatite coatings with different morphologies were produced on the surface of a titanium-niobium shape memory alloy via a hydrothermal process. The effect of the nano-hydroxyapatite coatings on the in vitro proliferation of SaOS-2 osteoblast-like cells was investigated. Factors including crystallinity, surface micro-roughness, and surface energy of the nano-hydroxyapatite coatings were discussed. Results show that in vitro proliferation of the osteoblast-like cells was significantly enhanced on the nano-hydroxyapatite-coated titanium-niobium alloy compared to the titanium-niobium alloy without coating. The cell numbers on the nano-hydroxyapatite-coated titanium-niobium alloy changed consistently with the surface energy of the hydroxyapatite coatings. This study suggests that surface energy as a characteristic parameter influencing the in vitro proliferation of osteoblast-like cells was predominant over the crystallinity and surface micro-roughness of the nano-hydroxyapatite coatings.

Establishment of HIV-1 latency in resting CD4+ T cells depends on chemokine-induced changes in the actin cytoskeleton

Eradication of HIV-1 with highly active antiretroviral therapy (HAART) is not possible due to the persistence of long-lived, latently infected resting memory CD4+ T cells. We now show that HIV-1 latency can be established in resting CD4+ T cells infected with HIV-1 after exposure to ligands for CCR7 (CCL19), CXCR3 (CXCL9 and CXCL10), and CCR6 (CCL20) but not in unactivated CD4+ T cells. The mechanism did not involve cell activation or significant changes in gene expression, but was associated with rapid dephosphorylation of cofilin and changes in filamentous actin. Incubation with chemokine before infection led to efficient HIV-1 nuclear localization and integration and this was inhibited by the actin stabilizer jasplakinolide. We propose a unique pathway for establishment of latency by direct HIV-1 infection of resting CD4+ T cells during normal chemokine-directed recirculation of CD4+ T cells between blood and tissue.

DeepCare: a deep dynamic memory model for predictive medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.